Think2Go: Generative Next POI Recommendation with LLM Reasoning

2026-07-30

AI Summary

This work addresses the limitations of existing next point-of-interest (POI) recommendation methods in capturing users’ deep intent from sparse mobility behaviors and the suboptimal performance of large language models due to inadequate understanding of semantic IDs (SIDs). To overcome these challenges, we propose Think2Go, a framework that jointly optimizes supervised fine-tuning and reinforcement learning to enhance SID representation learning. It further incorporates test-time compute scaling to explore diverse spatiotemporal patterns. Innovatively integrating memory mechanisms with adaptive inference, Think2Go introduces two advantage-weighting strategies—uncertainty-aware exploration guided by prompt-induced cognitive uncertainty and advantage scaling via reward normalization—to implicitly implement curriculum learning and prevent policy entropy collapse. Experiments demonstrate that our approach significantly improves behavior pattern retention, diversity in preference exploration, and training stability, achieving more robust and accurate performance on next POI recommendation.

Abstract

Next Point-of-Interest (POI) recommendation task focuses on mining user behavioral preference patterns from historical check-ins to provide personalized suggestions for the next destination. Existing methods primarily rely on shallow contextual information and handcrafted feature interactions to predict the next POI. However, the inherent sparsity and complexity of user mobility patterns limit the computational capacity of non-reasoning models to capture deep intent, while large language models (LLMs) perform suboptimally because they lack a deep understanding of semantic IDs (SIDs) when SIDs are trained separately. To address these limitations, we propose Think2Go, a novel generative next POI recommendation framework, which enhances the model's comprehension of SID representations and explores diverse spatial-temporal patterns via test-time computational scaling. We unify supervised fine-tuning (SFT) and reinforcement learning (RL)-based reasoning within a single architecture, enabling joint optimization of memorization and adaptive reasoning to better retain user behavior patterns while exploring diverse user preferences. To further calibrate policy optimization in adaptive reasoning, we propose two advantage weighting mechanisms that integrate (1) prompt epistemic uncertainty, estimated via kernel density methods to assess the spatial-temporal periodic pattern alignment between queries and user history, promoting increased exploration under high epistemic uncertainty; and (2) reward-informed advantage scaling, captured by normalizing rewards against their maxima to adapt update magnitudes, thereby improving training stability and mitigating overfitting to noisy signals. This joint calibration forms an implicit curriculum learning strategy, delivering fine-grained, instance-aware policy updates that prevent entropy collapse and support robust exploration.

Problem

• next POI recommendation
• user mobility patterns
• semantic IDs
• large language models
• sparsity

Innovation

• Generative POI Recommendation
• LLM Reasoning
• Reinforcement Learning
• Semantic ID Representation
• Curriculum Learning